EFFECTS OF REVENUE COLLECTION AUTOMATION ON THE PERFORMANCE OF OWN SOURCE REVENUE IN NYANDARUA COUNTY KENYA
EFFECTS OF REVENUE COLLECTION AUTOMATION ON THE PERFORMANCE OF OWN SOURCE REVENUE IN NYANDARUA COUNTY KENYA
Laban Muchiri W - Master’s Student, Department of Public Policy and Administration, Kenyatta University, Kenya
Dr. Moses Muthinja - Lecturer, Department of Public Policy and Administration, Kenyatta University, Kenya
ABSTRACT
Revenue collection at county level improves service delivery by the county government. Automated systems have been proven to be more efficient than the conventional methods of revenue collection. The research is motivated by this backdrop to establish the effects of automation on revenue collection in Nyandarua County, Kenya. The research aimed to establish the effects of automation of revenue collection, mobile payments, online response process and integrated tax management systems on the own source revenue collection in Nyandarua County. The survey is directed by the Resource Based Views Theory and the Transaction Cost Theory. The study targets 12 officials and 40 staff from the Department of Financial Reporting and Accounting as well as from the ICT Department in the County Government of Nyandarua. The units of analysis were the County Revenue Director, 5 Sub-County Revenue Officers as well as 20 support staff directly involved in the collection of revenue across Nyandarua County. From the ICT department, the units of observation were the ICT Director, 5 ICT Officers in charge of each of the Sub Counties as well as 20 ICT support staff. Primary data was gathered by dispatching questionnaires to the participants while secondary data was acquired from the financial records of the County. The SPSS software was utilized to analyze the data. The inferential and descriptive statistics were utilized to statistically examine quantitative data, while content analysis was utilized to statistically evaluate qualitative data. The outcomes were presented in tables and figures utilizing frequencies, mean, and standard deviation. The research utilized a multivariate regression model to show how the factors were correlated. Tables, charts, and graphs were used to display the findings. At the 95% level of significance, the results established a R squared of 0.862 and an adjusted R squared of 0.849, indicating that automation of revenue collection jointly accounts for 84.9% of the variation in own source revenue collection in Nyandarua County. Also, the research established that mobile payments had a positive significant effect on own source revenue collection in Nyandarua County (β =.538, p=.005<.05), online response process had a positive significant effect on own source revenue collection in Nyandarua County (β =.239, p=.042<.05) and finally integration of tax management systems had a positive significant effect on the own source revenue collection in Nyandarua County (β =.282, p=.019>.05). The research concluded that the automation of revenue collection had positive significant effect on own source revenue collection in Nyandarua County. To enhance the collection of revenue from their own sources, the report advised other Counties to investigate automating revenue collection.
